增强CT联合纹理分析鉴别胰头肿块型胰腺炎与胰头癌价值分析  

Contrast-enhanced CT and texture analysis of mass-forming pancreatitis and cancer in the pancreatic head

作　　者：张晶晶 李庆祝 王建华 陈晓 任帅 叶丹丹 张惠峰 王中秋 Zhang Jingjing;Li Qingzhu;Wang Jianhua;Chen Xiao;Ren Shuai;Ye Dandan;Zhang Huifeng;Wang Zhongqiu(Department of Radiology, Jiangsu Province Hospital of Chinese Medicine, Nanjing University of Chinese Medicine, Nanjing 210029, China;Department of Radiology, Yijishan Hospital of Wannan Medical College, Wuhu 241001, China)

机构地区：[1]南京中医药大学附属医院放射科,南京210029 [2]皖南医学院第一附属医院放射科,芜湖241001

出　　处：《中华医学杂志》2019年第33期2575-2580,共6页National Medical Journal of China

基　　金：国家自然科学基金面上项目(81771899);江苏省重点研发计划(社会发展)项目(BE2017772);2018年江苏省研究生科研创新计划(KYCX18_1651).

摘　　要：目的探讨增强CT征象联合纹理分析参数鉴别胰头肿块型胰腺炎与胰头癌的价值。方法回顾性收集2014年1月至2017年12月间南京中医药大学附属医院及皖南医学院第一附属医院经手术或活检病理证实的21例胰头肿块型胰腺炎与47例胰头导管腺癌患者。统计患者的性别、年龄等一般资料和CT征象,选择胰实质期进行纹理分析。对纹理参数进行最小绝对收缩和选择算子(LASSO)法进行降维处理。根据Shapiro-Wilks正态性检验结果,对连续型变量采用两独立样本t检验或Mann-WhitneyU检验。分类变量采用χ2或Fisher精确概率检验。运用多因素回归分析,建立CT征象、CT纹理分析、CT征象加纹理分析预测模型。受试者工作特征(ROC)曲线用于评价单个指标和各预测模型的诊断效能,Delong检验用于比较各模型的曲线下面积(AUC)值差异是否有统计学意义。结果CT征象预测模型由胰实质期病灶CT值和胰管贯穿征组成,纹理分析预测模型由均方根和135°方向低灰度游程优势(lowgreylevelrunemphasis_angle135)组成,二者的AUC值差异无统计学意义(Z=0.150,P>0.05)。CT征象和纹理分析联合的预测模型诊断效能最高(AUC值0.944,敏感度83.0%,特异度95.2%,阳性似然比17.43,阴性似然比0.18),同CT征象预测模型(Z=2.008,P<0.05)和纹理分析预测模型(Z=2.236,P<0.05)差异均有统计学意义。结论CT征象模型和纹理分析模型对胰头肿块型胰腺炎和胰头癌具有一定的鉴别诊断价值,增强CT联合纹理分析模型具有最好的诊断效能,可以进一步提高鉴别诊断能力。Objective To explore the value of contrast-enhanced CT combined with texture analysis in differentiating pancreatic cancer from mass-forming pancreatitis in pancreatic head. Methods A retrospective study collected 21 patients with pancreatic head mass-forming pancreatitis confirmed by surgery or biopsy and 47 patients with pancreatic ductal adenocarcinoma confirmed by surgery. The patients visited the Affiliated Hospital of Nanjing University of Chinese Medicine and the First Affiliated Hospital of Wannan Medical College between January 2014 and December 2017. Gender, age and CT findings were collected. The parenchymal phase was selected for texture analysis. The minimum absolute shrinkage and selection operator (LASSO) method was applied for dimensionality reduction.Two independent sample t-tests or Mann-Whitney U test were used for continuous variables based on the Shapiro-Wilks normality test results. Categorical variables were tested by Chi-square or Fisher test. By multivariable regression analysis, CT findings, CT texture analysis, CT findings combined with texture analysis prediction models were established. The receiver operating characteristic (ROC) curve was used to evaluate the diagnostic performance of individual indicators and each prediction model. The Delong test was used to compare the area under the curve (AUC) of each model. Results The CT findings prediction model consisted of CT value of lesion on pancreatic parenchymal phase and pancreatic duct penetrating sign. The texture analysis prediction model consists of root mean square and low grey level run emphasis_angle135. The AUC of them were not statistically different (Z=0.150,P>0.05). The combined predictive model had the better diagnostic performance (AUC 0.944, sensitivity 83.0%, specificity 95.2%,+LR 17.43,-LR 0.18) than CT sign prediction model (Z=2.008, P<0.05) and texture analysis prediction model(Z=2.236, P<0.05) were significantly different. Conclusions The CT findings model and the texture analysis model have equivalent diagnostic pe

关 键 词：胰腺肿瘤 胰腺炎 体层摄影术 X线计算机 纹理分析 

分 类 号：R576[医药卫生—消化系统] R735.9[医药卫生—内科学]